1. Technical Field
The present disclosure relates generally to the control of mechanical systems and, more particularly, to the utilization of neural networks that provide enhanced control of mechanical systems.
2. Related Art
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent it is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
Mechanical systems are typically operated or “driven” by a control signal that applies power to the mechanical system. In systems that implement feedback control, the response of the mechanical system to the control signal is measured and the control signal is correctively adjusted (i.e., compensated) based on a feedback signal representative of the measured response in an attempt to obtain the desired response of the mechanical system. In other words, the control signal is corrected in order to track changes in the actual response of the system and to suppress unmeasured disturbances that arise during operation. As such, the utilization of feedback control generally results in operational periods in which the actual response is not at the desired response (i.e., setpoint). This may lead to unsatisfactory performance particularly in systems having substantial amounts of delay and/or large time constants. Nonetheless, the control of a number of mechanical systems is entirely provided via feedback. Though most mechanical systems require a degree of feedback as a component of the control signal to account for the unforeseen and/or poor behavior of the mechanical system, the majority of the control signal (i.e., the driving signal) may be provided via feed forward control thereby reducing the reliance of the system on feedback control.
Feed forward control allows for the prediction of a control signal necessary to achieve a desired response from a system. Feed forward control enables known disturbances experienced by a system to be measured and accounted for prior to the disturbances affecting the system response. However, the cost of implementing and maintaining feed forward control in a system can be substantial. Feed forward control typically requires the development of a feed forward model that closely models the actual response of a system over a substantially representative subset of input values of an operating range of the system. A given system may comprise many layers of variable elements such as motors, gears, friction, damping, and mass and may operate amidst various environmental conditions that all contribute to and/or influence a composite nature of the system. Thus, the development of a feed forward model for a given system operating in a multi-input space may be computationally complex and may suffer from inaccuracies, thereby negatively impacting the ability to precisely control the operation of the system.